UK crime data test

library(ukpolice)
library(tidyverse)

Registered S3 methods overwritten by 'dbplyr':
  method         from
  print.tbl_lazy     
  print.tbl_sql      
-- Attaching packages ---------------------------- tidyverse 1.3.1 --
v ggplot2 3.3.5     v purrr   0.3.4
v tibble  3.1.6     v dplyr   1.0.7
v tidyr   1.2.0     v stringr 1.4.0
v readr   2.1.2     v forcats 0.5.1
-- Conflicts ------------------------------- tidyverse_conflicts() --
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()

library(leaflet)

Registered S3 methods overwritten by 'htmltools':
  method               from         
  print.html           tools:rstudio
  print.shiny.tag      tools:rstudio
  print.shiny.tag.list tools:rstudio
Registered S3 method overwritten by 'htmlwidgets':
  method           from         
  print.htmlwidget tools:rstudio

library(leaflet.extras)

date_df <- expand.grid(year = 2020:2021,
                       month = 1:12)

dates <- paste0(date_df$year, "-",
                str_pad(date_df$month, width = 2, pad = "0"))
dates

 [1] "2020-01" "2021-01" "2020-02" "2021-02" "2020-03" "2021-03"
 [7] "2020-04" "2021-04" "2020-05" "2021-05" "2020-06" "2021-06"
[13] "2020-07" "2021-07" "2020-08" "2021-08" "2020-09" "2021-09"
[19] "2020-10" "2021-10" "2020-11" "2021-11" "2020-12" "2021-12"

local_crimes_mh <- lapply(dates,
                       \(d) ukc_crime_coord(lat = 51.5709,
                                            lng = -0.0960,
                                            date = d)) %>%
  bind_rows()

local_crimes_mh %>%
  group_by(category) %>%
  summarise(n = n()) %>%
  mutate(perc = (n*100/sum(n)) %>% round(0)) %>%
  arrange(desc(perc))

local_crimes_mh %>%
  leaflet() %>%
  addTiles() %>%
  addHeatmap(
    lng = ~ longitude %>% as.numeric(),
    lat = ~ latitude  %>% as.numeric()
  )

map_crimes <- function(dat) {
  dat %>%
  leaflet() %>%
  addTiles() %>%
  addCircleMarkers(
    lng = ~ longitude %>% as.numeric() %>% jitter(),
    lat = ~ latitude  %>% as.numeric() %>% jitter(),
    popup = ~ paste0(
      "<B>", category, "</B>",
      "<BR>",
      street_name,
      "<BR>",
      month,
      "<BR><BR>",
      "Outcome: ", outcome_status_category,
      "<BR>(",
      outcome_status_date, ")"
    ),
    radius = 1
  )
}

local_crimes_mh %>%
  map_crimes()

num_10_crimes <- lapply(dates,
                       \(d) ukc_crime_coord(lat = 51.5034,
                                            lng = -0.1276,
                                            date = d)) %>%
  bind_rows()

num_10_crimes %>%
  map_crimes()

num_10_crimes %>%
  filter(str_detect(street_name, "On or near Downing Street")) %>%
  group_by(month, category) %>%
  summarise(n = n())

`summarise()` has grouped output by 'month'. You can override using the `.groups` argument.